Text-independent speaker verification is an interesting task where the use of Gaussian Mixture Models is almost a must. Nevertheless, some preliminar encouraging results obtained in previous works using ANN in speaker verification have led us to consider to perform a direct comparison between these different methods. In this sense, this paper is only focused on the classification stage of both GMM-based and ANNbased speaker verification systems. Experiments are accomplish making use of the AHUMADA/GAUDI spanish speech database, specially oriented for speaker-recognition tasks as it contains multisession and multichannel data of about 500 speakers. Results confirm a better performance when using GMM-based system and microphonic speech but, on the other hand, when testing in specific conditions and with real telephone speech ANN outperforms GMM results.
Cite as: Vivaracho, C.E., Ortega-García, J., Alonso, L., Moro, Q.I. (2001) A comparative study of MLP-based artificial neural networks in text-independent speaker verification against GMM-based systems. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 1753-1757, doi: 10.21437/Eurospeech.2001-410
@inproceedings{vivaracho01_eurospeech,
author={Carlos E. Vivaracho and Javier Ortega-García and Luis Alonso and Quiliano I. Moro},
title={{A comparative study of MLP-based artificial neural networks in text-independent speaker verification against GMM-based systems}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={1753--1757},
doi={10.21437/Eurospeech.2001-410}
}